What You Need to Know Before You Download Jukebox AI
Billed as a complete music management system, the application allows you to organize your music collection, create playlists and access music downloads and streams from the well stocked Y!Unlimited portal. It pulls this off through an attractive, easy-to-use interface that looks and feels like the lovechild of iTunes and MusicMatch Jukebox.
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However, the biggest differences between the two apps arise in the integration of their respective download services. The Yahoo! Music Jukebox brings the online service and the client app together much tighter than iTunes does.Some of the cooler features include the ability to create personalized LAUNCHcast radio stations, build playlists based on these personal preferences, and the ability to share songs and playlists via Messenger or email.
Media Jukebox features a sleek dark interface organized in several areas and some extra details that add a spicy taste like the different 3D effects you can apply to the album cover art, the great smart playlist creation tool or the ability to download plug-ins from the developer's site to improve or add new functions to the program.
Best file organization of any digital music jukebox
Connect your iPod or PlaysForSure device
Play all popular music files, and audio podcasts
Rip, Burn and Encode to multiple formats
Create custom Playlists and Smartlists
Extensive Tagging capabilities
Automatic CD, Album art and Track look-up
Built-in music services, including the new Amazon MP3 store
Full speed Ripping and Burning
Powerful EQ, DSP and audio effects
Print custom CD labels and covers
If you stopped sampling at only the first level and want to upsample the saved codes, you can runpython jukebox/sample.py --model=5b_lyrics --name=sample_5b --levels=3 --mode=upsample \--codes_file=sample_5b/level_2/data.pth.tar --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 \--sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125Here, we take the 20 seconds samples saved from the first sampling run at sample_5b/level_2/data.pth.tar and upsample the lower two levels.
To train a small vqvae, runmpiexec -n ngpus python jukebox/train.py --hps=small_vqvae --name=small_vqvae --sample_length=262144 --bs=4 \--audio_files_dir=audio_files_dir --labels=False --train --aug_shift --aug_blendHere, audio_files_dir is the directory in which you can put the audio files for your dataset, and ngpus is number of GPU's you want to use to train. The above trains a two-level VQ-VAE with downs_t = (5,3), and strides_t = (2, 2) meaning we downsample the audio by 2**5 = 32 to get the first level of codes, and 2**8 = 256 to get the second level codes. Checkpoints are stored in the logs folder. You can monitor the training by running Tensorboardtensorboard --logdir logs
To train top-level on a new dataset, runmpiexec -n ngpus python jukebox/train.py --hps=vqvae,small_prior,all_fp16,cpu_ema --name=pretrained_vqvae_small_prior \--sample_length=1048576 --bs=4 --aug_shift --aug_blend --audio_files_dir=audio_files_dir \--labels=False --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000Training the small_prior with a batch size of 2, 4, and 8 requires 6.7 GB, 9.3 GB, and 15.8 GB of GPU memory, respectively. A few days to a week of training typically yields reasonable samples when the dataset is homogeneous (e.g. all piano pieces, songs of the same style, etc).
Next, in hparams.py, we add them to the registry with the corresponding restore_paths and any other command line options used during training. Another important note is that for top-level priors with lyric conditioning, we have to locate a self-attention layer that shows alignment between the lyric and music tokens. Look for layers where prior.prior.transformer._attn_mods[layer].attn_func is either 6 or 7. If your model is starting to sing along lyrics, it means some layer, head pair has learned alignment. Congrats!```mysmallvqvae = Hyperparams( restorevqvae='/path/to/jukebox/logs/smallvqvae/checkpointsomestep.pth.tar',)mysmallvqvae.update(smallvqvae)HPARAMSREGISTRY["mysmallvqvae"] = mysmallvqvae
mysmallprior = Hyperparams( restoreprior='/path/to/jukebox/logs/smallprior/checkpointlatest.pth.tar', level=1, labels=False, # TODO For the two lines below, if --labels was used and the model is # trained with lyrics, find and enter the layer, head pair that has learned # alignment. alignmentlayer=47, alignmenthead=0,)mysmallprior.update(smallprior)HPARAMSREGISTRY["mysmallprior"] = mysmall_prior
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After these modifications, to train a top-level with labels and lyrics, runmpiexec -n ngpus python jukebox/train.py --hps=vqvae,small_single_enc_dec_prior,all_fp16,cpu_ema --name=pretrained_vqvae_small_single_enc_dec_prior_labels \--sample_length=786432 --bs=4 --aug_shift --aug_blend --audio_files_dir=audio_files_dir \--labels=True --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000To simplify hps choices, here we used a single_enc_dec model like the 1b_lyrics model that combines both encoder and decoder of the transformer into a single model. We do so by merging the lyric vocab and vq-vae vocab into a single larger vocab, and flattening the lyric tokens and the vq-vae codes into a single sequence of length n_ctx + n_tokens. This uses attn_order=12 which includes prime_attention layers with keys/values from lyrics and queries from audio. If you instead want to use a model with the usual encoder-decoder style transformer, use small_sep_enc_dec_prior.
After these modifications, run mpiexec -n ngpus python jukebox/train.py --hps=vqvae,prior_1b_lyrics,all_fp16,cpu_ema --name=finetuned \--sample_length=1048576 --bs=1 --aug_shift --aug_blend --audio_files_dir=audio_files_dir \--labels=True --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000To get the best sample quality, it is recommended to anneal the learning rate in the end. Training the 5B top-level requires GPipe which is not supported in this release.
Rather than playing the music file directly from Dropbox, Jukebox downloads it, which means that once the app has downloaded a specific music file you can delete it from your Dropbox account and free up valuable space.
The My Jukebox app is a fun and easy way to play music at NSM jukebox venues. Simply download the app, create an account, add payment via PayPal, and chose the songs or music videos you want to play on the jukebox.
With the app, you can search for any nearby venues with and NSM jukebox. This makes planning a fun night out easy. Find the best venue and unleash the power of the app to create the perfect soundtrack for your night out.
With over 2.5 million downloads, the AMI Music app offers a cool, convenient, and easy way to play music on a nearby AMI jukebox. Simply create an account, add funds for credits using a credit card, Apple Pay, or PayPal, select your current venue, and choose the songs or music videos you want to hear played. With the Multi-Select option, you can even add several songs or entire playlists to the jukebox queue in one transaction.
Unlike dropping a few quarters into a jukebox, the music credits you purchase through our app can be used across multiple visits, as well as multiple venues. With tens of thousands of venues across North America, AMI Music ensures your favorite music follows you wherever you go!
MusicMatch Jukebox is a slick software that requires less storage than many software.Since we added this program to our catalog in 2012, it has obtained 17,525 downloads, and last week it had 6 downloads.MusicMatch Jukebox is available for users with the operating system Mac OS Carbon and former versions, and you can get it only in English. Its current version is 3.0fc77 and was updated on 20/06/2012.
BoxEasy is a graphical jukebox style media player that uses album cover art to access songs. The screen looks much like a pub jukebox and works in a similar way. There is also a shuffle-all feature and the ability to create collections of albums. It deliberately uses file and folder names rather than tag information to avoid the repetition and errors caused by songs that have been poorly tagged.
Includes Jukebox, Shuffle and Album play modes, as well as a track search feature
Scans for existing albums and quickly adds new ones
Use the alphabetical navigation bar to access albums quickly, or search for individual songs
It can play randomly from all tracks or from custom collections
It has the ability to operate like a pub jukebox - great at parties
Plays MP3, WMA, OGG* and M4A* (*WM codec may be required)
Simply drag and drop tracks to create albums or add new songs
You can listen to online music and enjoy offline music with this application. The application will have a special music download support feature to download the music you want to the offline storage. Once you have downloaded these songs, you can listen to them anywhere, anytime, without connecting to the internet.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read thedocumentation from PretrainedConfig for more information. Instantiating a configuration with the defaults willyield a similar configuration to that ofopenai/jukebox-1b-lyrics architecture.
This is the configuration class to store the configuration of a JukeboxPrior. It is used to instantiate aJukeboxPrior according to the specified arguments, defining the model architecture. Instantiating aconfiguration with the defaults will yield a similar configuration to that of the top level prior from the[openai/jukebox-1b-lyrics]( -1b-lyrics) architecture.
This is the configuration class to store the configuration of a JukeboxVQVAE. It is used to instantiate aJukeboxVQVAE according to the specified arguments, defining the model architecture. Instantiating a configurationwith the defaults will yield a similar configuration to that of the VQVAE fromopenai/jukebox-1b-lyrics architecture.